Overview

Dataset statistics

Number of variables43
Number of observations1649
Missing cells14434
Missing cells (%)20.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory764.1 B

Variable types

NUM20
BOOL16
CAT7

Warnings

apacheadmissiondx has constant value "1649" Constant
age has a high cardinality: 70 distinct values High cardinality
dischargeweight is highly correlated with admissionweightHigh correlation
admissionweight is highly correlated with dischargeweightHigh correlation
hospitaldischargestatus is highly correlated with hospitaldischargelocationHigh correlation
hospitaldischargelocation is highly correlated with hospitaldischargestatusHigh correlation
admissionheight has 27 (1.6%) missing values Missing
hospitaldischargelocation has 27 (1.6%) missing values Missing
hospitaldischargestatus has 23 (1.4%) missing values Missing
admissionweight has 46 (2.8%) missing values Missing
dischargeweight has 730 (44.3%) missing values Missing
min_nibp has 200 (12.1%) missing values Missing
max_nibp has 200 (12.1%) missing values Missing
avg_nibp has 200 (12.1%) missing values Missing
min_ibp has 1450 (87.9%) missing values Missing
max_ibp has 1450 (87.9%) missing values Missing
avg_ibp has 1450 (87.9%) missing values Missing
min_nibp_systolic has 129 (7.8%) missing values Missing
max_nibp_systolic has 129 (7.8%) missing values Missing
avg_nibp_systolic has 129 (7.8%) missing values Missing
min_ibp_systolic has 1504 (91.2%) missing values Missing
max_ibp_systolic has 1504 (91.2%) missing values Missing
avg_ibp_systolic has 1504 (91.2%) missing values Missing
creatinine has 22 (1.3%) missing values Missing
gcs has 232 (14.1%) missing values Missing
antihypertensive_iv_tight_control has 447 (27.1%) missing values Missing
antihypertensive_iv_non_tight_control has 447 (27.1%) missing values Missing
heparin_iv has 447 (27.1%) missing values Missing
inotropes has 447 (27.1%) missing values Missing
antiplatelets has 447 (27.1%) missing values Missing
anticoag has 447 (27.1%) missing values Missing
antihypertensive_non_iv has 447 (27.1%) missing values Missing
hypertension has 44 (2.7%) missing values Missing
ischemic_heart_disease has 44 (2.7%) missing values Missing
peripheral_vascular_disease has 44 (2.7%) missing values Missing
atrial_fibrillation has 44 (2.7%) missing values Missing
transient_ischemic_attack has 44 (2.7%) missing values Missing
previous_stroke has 44 (2.7%) missing values Missing
diabetes has 44 (2.7%) missing values Missing
hosp_mortality has 23 (1.4%) missing values Missing
patientunitstayid has unique values Unique

Reproduction

Analysis started2020-12-13 06:21:32.575762
Analysis finished2020-12-13 06:23:29.229290
Duration1 minute and 56.65 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

patientunitstayid
Real number (ℝ≥0)

UNIQUE

Distinct1649
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1807941.817
Minimum142974
Maximum3353094
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:29.420936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum142974
5-th percentile265616
Q1999078
median1643521
Q32817963
95-th percentile3226636.2
Maximum3353094
Range3210120
Interquartile range (IQR)1818885

Descriptive statistics

Standard deviation1001607.326
Coefficient of variation (CV)0.5540041811
Kurtosis-1.344272056
Mean1807941.817
Median Absolute Deviation (MAD)941529
Skewness0.06850907624
Sum2981296056
Variance1.003217235e+12
MonotocityNot monotonic
2020-12-13T14:23:29.685613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
14949710.1%
 
236271510.1%
 
323922710.1%
 
45394910.1%
 
157420610.1%
 
85126710.1%
 
168071010.1%
 
156846910.1%
 
275182510.1%
 
131617810.1%
 
318395610.1%
 
71816610.1%
 
181794910.1%
 
224184210.1%
 
308951910.1%
 
161315710.1%
 
76733610.1%
 
157835010.1%
 
156451110.1%
 
321854510.1%
 
100697110.1%
 
49906910.1%
 
107046410.1%
 
50726710.1%
 
59116010.1%
 
Other values (1624)162498.5%
 
ValueCountFrequency (%) 
14297410.1%
 
14344810.1%
 
14359610.1%
 
14548810.1%
 
14757510.1%
 
14817710.1%
 
14949710.1%
 
14960610.1%
 
14992210.1%
 
15088610.1%
 
ValueCountFrequency (%) 
335309410.1%
 
335233110.1%
 
335197310.1%
 
335124810.1%
 
335085310.1%
 
335052010.1%
 
335039510.1%
 
335020510.1%
 
334884110.1%
 
334799110.1%
 

gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
Male
861 
Female
788 
ValueCountFrequency (%) 
Male86152.2%
 
Female78847.8%
 
2020-12-13T14:23:29.912472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-13T14:23:30.054025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:23:30.228909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length4.955730746
Min length4

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e243729.8%
 
a164920.2%
 
l164920.2%
 
M86110.5%
 
F7889.6%
 
m7889.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter652379.8%
 
Uppercase Letter164920.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M86152.2%
 
F78847.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e243737.4%
 
a164925.3%
 
l164925.3%
 
m78812.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin8172100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e243729.8%
 
a164920.2%
 
l164920.2%
 
M86110.5%
 
F7889.6%
 
m7889.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII8172100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e243729.8%
 
a164920.2%
 
l164920.2%
 
M86110.5%
 
F7889.6%
 
m7889.6%
 

age
Categorical

HIGH CARDINALITY

Distinct70
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
> 89
 
75
75
 
54
71
 
49
87
 
48
66
 
47
Other values (65)
1376 
ValueCountFrequency (%) 
> 89754.5%
 
75543.3%
 
71493.0%
 
87482.9%
 
66472.9%
 
73462.8%
 
79462.8%
 
83462.8%
 
72452.7%
 
81432.6%
 
78422.5%
 
69412.5%
 
61402.4%
 
68402.4%
 
59392.4%
 
86392.4%
 
76382.3%
 
74372.2%
 
82372.2%
 
57372.2%
 
77362.2%
 
65362.2%
 
70362.2%
 
88362.2%
 
84352.1%
 
Other values (45)58135.2%
 
2020-12-13T14:23:30.483517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)0.2%
2020-12-13T14:23:30.707283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.090964221
Min length2

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
860317.5%
 
759717.3%
 
652815.3%
 
541912.2%
 
42517.3%
 
92477.2%
 
32086.0%
 
21704.9%
 
11634.7%
 
01123.2%
 
>752.2%
 
752.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number329895.6%
 
Math Symbol752.2%
 
Space Separator752.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
860318.3%
 
759718.1%
 
652816.0%
 
541912.7%
 
42517.6%
 
92477.5%
 
32086.3%
 
21705.2%
 
11634.9%
 
01123.4%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>75100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
75100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3448100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
860317.5%
 
759717.3%
 
652815.3%
 
541912.2%
 
42517.3%
 
92477.2%
 
32086.0%
 
21704.9%
 
11634.7%
 
01123.2%
 
>752.2%
 
752.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3448100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
860317.5%
 
759717.3%
 
652815.3%
 
541912.2%
 
42517.3%
 
92477.2%
 
32086.0%
 
21704.9%
 
11634.7%
 
01123.2%
 
>752.2%
 
752.2%
 

ethnicity
Categorical

Distinct6
Distinct (%)0.4%
Missing7
Missing (%)0.4%
Memory size13.0 KiB
Caucasian
1275 
African American
197 
Other/Unknown
 
77
Hispanic
 
68
Asian
 
16
ValueCountFrequency (%) 
Caucasian127577.3%
 
African American19711.9%
 
Other/Unknown774.7%
 
Hispanic684.1%
 
Asian161.0%
 
Native American90.5%
 
(Missing)70.4%
 
2020-12-13T14:23:30.911361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-13T14:23:31.052012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:23:31.239155image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length9
Mean length9.950272893
Min length3

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a432826.4%
 
n200712.2%
 
i183911.2%
 
c174610.6%
 
s13598.3%
 
C12757.8%
 
u12757.8%
 
r4802.9%
 
A4192.6%
 
e2921.8%
 
2061.3%
 
m2061.3%
 
f1971.2%
 
t860.5%
 
O770.5%
 
h770.5%
 
/770.5%
 
U770.5%
 
k770.5%
 
o770.5%
 
w770.5%
 
H680.4%
 
p680.4%
 
N90.1%
 
v90.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1420086.5%
 
Uppercase Letter192511.7%
 
Space Separator2061.3%
 
Other Punctuation770.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C127566.2%
 
A41921.8%
 
O774.0%
 
U774.0%
 
H683.5%
 
N90.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a432830.5%
 
n200714.1%
 
i183913.0%
 
c174612.3%
 
s13599.6%
 
u12759.0%
 
r4803.4%
 
e2922.1%
 
m2061.5%
 
f1971.4%
 
t860.6%
 
h770.5%
 
k770.5%
 
o770.5%
 
w770.5%
 
p680.5%
 
v90.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
206100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/77100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1612598.3%
 
Common2831.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a432826.8%
 
n200712.4%
 
i183911.4%
 
c174610.8%
 
s13598.4%
 
C12757.9%
 
u12757.9%
 
r4803.0%
 
A4192.6%
 
e2921.8%
 
m2061.3%
 
f1971.2%
 
t860.5%
 
O770.5%
 
h770.5%
 
U770.5%
 
k770.5%
 
o770.5%
 
w770.5%
 
H680.4%
 
p680.4%
 
N90.1%
 
v90.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
20672.8%
 
/7727.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII16408100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a432826.4%
 
n200712.2%
 
i183911.2%
 
c174610.6%
 
s13598.3%
 
C12757.8%
 
u12757.8%
 
r4802.9%
 
A4192.6%
 
e2921.8%
 
2061.3%
 
m2061.3%
 
f1971.2%
 
t860.5%
 
O770.5%
 
h770.5%
 
/770.5%
 
U770.5%
 
k770.5%
 
o770.5%
 
w770.5%
 
H680.4%
 
p680.4%
 
N90.1%
 
v90.1%
 

apacheadmissiondx
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
CVA, cerebrovascular accident/stroke
1649 
ValueCountFrequency (%) 
CVA, cerebrovascular accident/stroke1649100.0%
 
2020-12-13T14:23:31.427805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-13T14:23:31.550312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:23:31.700496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length36
Mean length36
Min length36

Overview of Unicode Properties

Unique unicode characters21
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
c659611.1%
 
e659611.1%
 
r659611.1%
 
a49478.3%
 
32985.6%
 
o32985.6%
 
s32985.6%
 
t32985.6%
 
C16492.8%
 
V16492.8%
 
A16492.8%
 
,16492.8%
 
b16492.8%
 
v16492.8%
 
u16492.8%
 
l16492.8%
 
i16492.8%
 
d16492.8%
 
n16492.8%
 
/16492.8%
 
k16492.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4782180.6%
 
Uppercase Letter49478.3%
 
Other Punctuation32985.6%
 
Space Separator32985.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C164933.3%
 
V164933.3%
 
A164933.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,164950.0%
 
/164950.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3298100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
c659613.8%
 
e659613.8%
 
r659613.8%
 
a494710.3%
 
o32986.9%
 
s32986.9%
 
t32986.9%
 
b16493.4%
 
v16493.4%
 
u16493.4%
 
l16493.4%
 
i16493.4%
 
d16493.4%
 
n16493.4%
 
k16493.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5276888.9%
 
Common659611.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
c659612.5%
 
e659612.5%
 
r659612.5%
 
a49479.4%
 
o32986.2%
 
s32986.2%
 
t32986.2%
 
C16493.1%
 
V16493.1%
 
A16493.1%
 
b16493.1%
 
v16493.1%
 
u16493.1%
 
l16493.1%
 
i16493.1%
 
d16493.1%
 
n16493.1%
 
k16493.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
329850.0%
 
,164925.0%
 
/164925.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII59364100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
c659611.1%
 
e659611.1%
 
r659611.1%
 
a49478.3%
 
32985.6%
 
o32985.6%
 
s32985.6%
 
t32985.6%
 
C16492.8%
 
V16492.8%
 
A16492.8%
 
,16492.8%
 
b16492.8%
 
v16492.8%
 
u16492.8%
 
l16492.8%
 
i16492.8%
 
d16492.8%
 
n16492.8%
 
/16492.8%
 
k16492.8%
 

admissionheight
Real number (ℝ≥0)

MISSING

Distinct126
Distinct (%)7.8%
Missing27
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean169.2375092
Minimum61.5
Maximum205.7
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:31.906361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum61.5
5-th percentile152.4
Q1160.4
median170
Q3177.8
95-th percentile187.9
Maximum205.7
Range144.2
Interquartile range (IQR)17.4

Descriptive statistics

Standard deviation11.35615773
Coefficient of variation (CV)0.06710189588
Kurtosis7.256187305
Mean169.2375092
Median Absolute Deviation (MAD)7.8
Skewness-0.9083799916
Sum274503.24
Variance128.9623183
MonotocityNot monotonic
2020-12-13T14:23:32.141408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1601227.4%
 
167.6925.6%
 
177.8865.2%
 
172.7764.6%
 
162.6744.5%
 
170.2734.4%
 
165.1724.4%
 
157.5714.3%
 
182.9603.6%
 
180.3593.6%
 
175.3523.2%
 
165422.5%
 
154.9422.5%
 
188402.4%
 
180382.3%
 
175372.2%
 
152.4362.2%
 
170352.1%
 
173291.8%
 
185.4281.7%
 
178261.6%
 
163211.3%
 
168181.1%
 
185171.0%
 
172161.0%
 
Other values (101)36021.8%
 
(Missing)271.6%
 
ValueCountFrequency (%) 
61.510.1%
 
8510.1%
 
9410.1%
 
121.910.1%
 
134.610.1%
 
137.210.1%
 
14010.1%
 
142.250.3%
 
144.810.1%
 
14530.2%
 
ValueCountFrequency (%) 
205.710.1%
 
203.210.1%
 
202.210.1%
 
200.710.1%
 
198.110.1%
 
195.620.1%
 
193.0410.1%
 
193120.7%
 
19210.1%
 
19120.1%
 

hospitaldischargelocation
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.5%
Missing27
Missing (%)1.6%
Memory size13.0 KiB
Home
624 
Skilled Nursing Facility
281 
Rehabilitation
242 
Death
167 
Other
121 
Other values (3)
187 
ValueCountFrequency (%) 
Home62437.8%
 
Skilled Nursing Facility28117.0%
 
Rehabilitation24214.7%
 
Death16710.1%
 
Other1217.3%
 
Other External985.9%
 
Other Hospital613.7%
 
Nursing Home281.7%
 
(Missing)271.6%
 
2020-12-13T14:23:32.400280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-13T14:23:32.548617image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:23:32.754468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length5
Mean length10.13402062
Min length3

Overview of Unicode Properties

Unique unicode characters29
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i193911.6%
 
e172010.3%
 
t13718.2%
 
l12447.4%
 
a11186.7%
 
o9555.7%
 
7494.5%
 
H7134.3%
 
n7034.2%
 
h6894.1%
 
r6874.1%
 
m6523.9%
 
s3702.2%
 
N3091.8%
 
u3091.8%
 
g3091.8%
 
S2811.7%
 
k2811.7%
 
d2811.7%
 
F2811.7%
 
c2811.7%
 
y2811.7%
 
O2801.7%
 
R2421.4%
 
b2421.4%
 
Other values (4)4242.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1359181.3%
 
Uppercase Letter237114.2%
 
Space Separator7494.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H71330.1%
 
N30913.0%
 
S28111.9%
 
F28111.9%
 
O28011.8%
 
R24210.2%
 
D1677.0%
 
E984.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i193914.3%
 
e172012.7%
 
t137110.1%
 
l12449.2%
 
a11188.2%
 
o9557.0%
 
n7035.2%
 
h6895.1%
 
r6875.1%
 
m6524.8%
 
s3702.7%
 
u3092.3%
 
g3092.3%
 
k2812.1%
 
d2812.1%
 
c2812.1%
 
y2812.1%
 
b2421.8%
 
x980.7%
 
p610.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
749100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1596295.5%
 
Common7494.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i193912.1%
 
e172010.8%
 
t13718.6%
 
l12447.8%
 
a11187.0%
 
o9556.0%
 
H7134.5%
 
n7034.4%
 
h6894.3%
 
r6874.3%
 
m6524.1%
 
s3702.3%
 
N3091.9%
 
u3091.9%
 
g3091.9%
 
S2811.8%
 
k2811.8%
 
d2811.8%
 
F2811.8%
 
c2811.8%
 
y2811.8%
 
O2801.8%
 
R2421.5%
 
b2421.5%
 
D1671.0%
 
Other values (3)2571.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
749100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII16711100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i193911.6%
 
e172010.3%
 
t13718.2%
 
l12447.4%
 
a11186.7%
 
o9555.7%
 
7494.5%
 
H7134.3%
 
n7034.2%
 
h6894.1%
 
r6874.1%
 
m6523.9%
 
s3702.2%
 
N3091.8%
 
u3091.8%
 
g3091.8%
 
S2811.7%
 
k2811.7%
 
d2811.7%
 
F2811.7%
 
c2811.7%
 
y2811.7%
 
O2801.7%
 
R2421.4%
 
b2421.4%
 
Other values (4)4242.5%
 

hospitaldischargestatus
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing23
Missing (%)1.4%
Memory size13.0 KiB
Alive
1459 
Expired
167 
ValueCountFrequency (%) 
Alive145988.5%
 
Expired16710.1%
 
(Missing)231.4%
 
2020-12-13T14:23:32.970080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-13T14:23:33.097803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:23:33.273534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length5
Mean length5.174651304
Min length3

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i162619.1%
 
e162619.1%
 
A145917.1%
 
l145917.1%
 
v145917.1%
 
E1672.0%
 
x1672.0%
 
p1672.0%
 
r1672.0%
 
d1672.0%
 
n460.5%
 
a230.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter690780.9%
 
Uppercase Letter162619.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A145989.7%
 
E16710.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i162623.5%
 
e162623.5%
 
l145921.1%
 
v145921.1%
 
x1672.4%
 
p1672.4%
 
r1672.4%
 
d1672.4%
 
n460.7%
 
a230.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin8533100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i162619.1%
 
e162619.1%
 
A145917.1%
 
l145917.1%
 
v145917.1%
 
E1672.0%
 
x1672.0%
 
p1672.0%
 
r1672.0%
 
d1672.0%
 
n460.5%
 
a230.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII8533100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i162619.1%
 
e162619.1%
 
A145917.1%
 
l145917.1%
 
v145917.1%
 
E1672.0%
 
x1672.0%
 
p1672.0%
 
r1672.0%
 
d1672.0%
 
n460.5%
 
a230.3%
 

unittype
Categorical

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
Med-Surg ICU
693 
Neuro ICU
631 
SICU
124 
MICU
85 
Cardiac ICU
 
62
Other values (3)
 
54
ValueCountFrequency (%) 
Med-Surg ICU69342.0%
 
Neuro ICU63138.3%
 
SICU1247.5%
 
MICU855.2%
 
Cardiac ICU623.8%
 
CCU-CTICU362.2%
 
CTICU150.9%
 
CSICU30.2%
 
2020-12-13T14:23:33.487542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-13T14:23:33.625078image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:23:33.826167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length9
Mean length9.658580958
Min length4

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
C183711.5%
 
U168510.6%
 
I164910.4%
 
r13868.7%
 
13868.7%
 
e13248.3%
 
u13248.3%
 
S8205.1%
 
M7784.9%
 
d7554.7%
 
-7294.6%
 
g6934.4%
 
N6314.0%
 
o6314.0%
 
a1240.8%
 
i620.4%
 
c620.4%
 
T510.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter745146.8%
 
Lowercase Letter636139.9%
 
Space Separator13868.7%
 
Dash Punctuation7294.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C183724.7%
 
U168522.6%
 
I164922.1%
 
S82011.0%
 
M77810.4%
 
N6318.5%
 
T510.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r138621.8%
 
e132420.8%
 
u132420.8%
 
d75511.9%
 
g69310.9%
 
o6319.9%
 
a1241.9%
 
i621.0%
 
c621.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-729100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1386100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1381286.7%
 
Common211513.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
C183713.3%
 
U168512.2%
 
I164911.9%
 
r138610.0%
 
e13249.6%
 
u13249.6%
 
S8205.9%
 
M7785.6%
 
d7555.5%
 
g6935.0%
 
N6314.6%
 
o6314.6%
 
a1240.9%
 
i620.4%
 
c620.4%
 
T510.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
138665.5%
 
-72934.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII15927100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
C183711.5%
 
U168510.6%
 
I164910.4%
 
r13868.7%
 
13868.7%
 
e13248.3%
 
u13248.3%
 
S8205.1%
 
M7784.9%
 
d7554.7%
 
-7294.6%
 
g6934.4%
 
N6314.0%
 
o6314.0%
 
a1240.8%
 
i620.4%
 
c620.4%
 
T510.3%
 

admissionweight
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct726
Distinct (%)45.3%
Missing46
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean82.75192764
Minimum32.02
Maximum224.4
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:34.055455image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum32.02
5-th percentile52.016
Q168
median80
Q394.1
95-th percentile122.465
Maximum224.4
Range192.38
Interquartile range (IQR)26.1

Descriptive statistics

Standard deviation22.10867881
Coefficient of variation (CV)0.2671681427
Kurtosis2.836327119
Mean82.75192764
Median Absolute Deviation (MAD)12.98
Skewness1.088504953
Sum132651.34
Variance488.7936789
MonotocityNot monotonic
2020-12-13T14:23:34.296113image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
80171.0%
 
63.5150.9%
 
90.7140.8%
 
74.8140.8%
 
68140.8%
 
81.6130.8%
 
100120.7%
 
72.5110.7%
 
82110.7%
 
72.6110.7%
 
104.3100.6%
 
86.290.5%
 
7090.5%
 
8790.5%
 
7390.5%
 
8180.5%
 
77.180.5%
 
88.570.4%
 
65.870.4%
 
77.1170.4%
 
8570.4%
 
54.470.4%
 
8470.4%
 
6170.4%
 
87.570.4%
 
Other values (701)135382.0%
 
(Missing)462.8%
 
ValueCountFrequency (%) 
32.0210.1%
 
3410.1%
 
36.410.1%
 
3710.1%
 
37.4210.1%
 
39.310.1%
 
40.410.1%
 
41.510.1%
 
4210.1%
 
43.210.1%
 
ValueCountFrequency (%) 
224.410.1%
 
204.510.1%
 
185.310.1%
 
18310.1%
 
182.910.1%
 
181.410.1%
 
176.910.1%
 
170.610.1%
 
166.410.1%
 
16010.1%
 

dischargeweight
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct605
Distinct (%)65.8%
Missing730
Missing (%)44.3%
Infinite0
Infinite (%)0.0%
Mean82.06846572
Minimum11.98
Maximum231
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:34.531805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum11.98
5-th percentile49.74
Q167.3
median78.8
Q394.95
95-th percentile123.039
Maximum231
Range219.02
Interquartile range (IQR)27.65

Descriptive statistics

Standard deviation23.58898997
Coefficient of variation (CV)0.2874306198
Kurtosis4.982163027
Mean82.06846572
Median Absolute Deviation (MAD)13.48
Skewness1.207634826
Sum75420.92
Variance556.4404476
MonotocityNot monotonic
2020-12-13T14:23:34.766533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
73.460.4%
 
73.150.3%
 
66.550.3%
 
86.340.2%
 
75.940.2%
 
79.240.2%
 
70.340.2%
 
77.340.2%
 
69.740.2%
 
65.740.2%
 
97.140.2%
 
72.140.2%
 
74.540.2%
 
97.940.2%
 
76.240.2%
 
84.440.2%
 
9140.2%
 
77.140.2%
 
92.340.2%
 
7040.2%
 
8040.2%
 
75.340.2%
 
83.630.2%
 
75.730.2%
 
7130.2%
 
Other values (580)81849.6%
 
(Missing)73044.3%
 
ValueCountFrequency (%) 
11.9810.1%
 
21.310.1%
 
22.810.1%
 
24.810.1%
 
25.810.1%
 
27.910.1%
 
28.110.1%
 
28.510.1%
 
28.810.1%
 
30.610.1%
 
ValueCountFrequency (%) 
23110.1%
 
22310.1%
 
22010.1%
 
188.910.1%
 
18310.1%
 
176.910.1%
 
170.510.1%
 
153.910.1%
 
147.810.1%
 
144.110.1%
 

icu_los_hours
Real number (ℝ≥0)

Distinct242
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.95451789
Minimum0
Maximum771
Zeros5
Zeros (%)0.3%
Memory size13.0 KiB
2020-12-13T14:23:35.748083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q124
median43
Q370
95-th percentile188
Maximum771
Range771
Interquartile range (IQR)46

Descriptive statistics

Standard deviation71.73444822
Coefficient of variation (CV)1.139464658
Kurtosis19.40796544
Mean62.95451789
Median Absolute Deviation (MAD)21
Skewness3.730992072
Sum103812
Variance5145.831061
MonotocityNot monotonic
2020-12-13T14:23:36.216982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
26422.5%
 
22412.5%
 
23402.4%
 
19352.1%
 
47332.0%
 
25311.9%
 
45311.9%
 
43311.9%
 
21301.8%
 
24281.7%
 
28261.6%
 
46261.6%
 
20261.6%
 
38251.5%
 
37251.5%
 
17241.5%
 
27241.5%
 
18231.4%
 
31231.4%
 
41231.4%
 
49231.4%
 
68221.3%
 
34211.3%
 
36211.3%
 
32211.3%
 
Other values (217)95457.9%
 
ValueCountFrequency (%) 
050.3%
 
1100.6%
 
270.4%
 
350.3%
 
480.5%
 
540.2%
 
680.5%
 
790.5%
 
880.5%
 
980.5%
 
ValueCountFrequency (%) 
77110.1%
 
57610.1%
 
55210.1%
 
55110.1%
 
53610.1%
 
52810.1%
 
52210.1%
 
50110.1%
 
48610.1%
 
48110.1%
 

is_primary
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
1
1236 
0
413 
ValueCountFrequency (%) 
1123675.0%
 
041325.0%
 
2020-12-13T14:23:51.576247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

min_nibp
Real number (ℝ≥0)

MISSING

Distinct112
Distinct (%)7.7%
Missing200
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean79.7605245
Minimum16
Maximum170
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:51.781647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile52.4
Q168
median78
Q390
95-th percentile112
Maximum170
Range154
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.48203365
Coefficient of variation (CV)0.2317190585
Kurtosis1.68711711
Mean79.7605245
Median Absolute Deviation (MAD)11
Skewness0.6121859319
Sum115573
Variance341.5855677
MonotocityNot monotonic
2020-12-13T14:23:52.002248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
76513.1%
 
77452.7%
 
74422.5%
 
82392.4%
 
87382.3%
 
73382.3%
 
81362.2%
 
75362.2%
 
68352.1%
 
86342.1%
 
63332.0%
 
66332.0%
 
71332.0%
 
78321.9%
 
72321.9%
 
64311.9%
 
85311.9%
 
88301.8%
 
69291.8%
 
67291.8%
 
80291.8%
 
83271.6%
 
70261.6%
 
90251.5%
 
91241.5%
 
Other values (87)61137.1%
 
(Missing)20012.1%
 
ValueCountFrequency (%) 
1610.1%
 
2130.2%
 
2810.1%
 
3110.1%
 
3210.1%
 
3610.1%
 
3730.2%
 
3920.1%
 
4030.2%
 
4120.1%
 
ValueCountFrequency (%) 
17010.1%
 
16110.1%
 
15910.1%
 
15810.1%
 
15210.1%
 
14920.1%
 
14810.1%
 
14420.1%
 
14310.1%
 
14110.1%
 

max_nibp
Real number (ℝ≥0)

MISSING

Distinct124
Distinct (%)8.6%
Missing200
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean111.2097999
Minimum57
Maximum222
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:52.217755image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile79
Q196
median109
Q3123
95-th percentile149
Maximum222
Range165
Interquartile range (IQR)27

Descriptive statistics

Standard deviation22.27413057
Coefficient of variation (CV)0.2002892785
Kurtosis1.742021786
Mean111.2097999
Median Absolute Deviation (MAD)14
Skewness0.8239422646
Sum161143
Variance496.1368928
MonotocityNot monotonic
2020-12-13T14:23:52.531681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
106342.1%
 
104332.0%
 
105332.0%
 
108321.9%
 
114321.9%
 
109311.9%
 
94311.9%
 
110311.9%
 
115311.9%
 
113301.8%
 
95301.8%
 
116301.8%
 
107281.7%
 
121281.7%
 
101281.7%
 
117281.7%
 
100271.6%
 
112261.6%
 
98261.6%
 
96251.5%
 
97241.5%
 
92241.5%
 
118231.4%
 
124221.3%
 
102211.3%
 
Other values (99)74144.9%
 
(Missing)20012.1%
 
ValueCountFrequency (%) 
5710.1%
 
6210.1%
 
6410.1%
 
6520.1%
 
6730.2%
 
6810.1%
 
6960.4%
 
7040.2%
 
7150.3%
 
7260.4%
 
ValueCountFrequency (%) 
22210.1%
 
21710.1%
 
21610.1%
 
20710.1%
 
20510.1%
 
20310.1%
 
19810.1%
 
19310.1%
 
19010.1%
 
18510.1%
 

avg_nibp
Real number (ℝ≥0)

MISSING

Distinct1036
Distinct (%)71.5%
Missing200
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean93.66427727
Minimum52
Maximum170
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:52.738998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile69.21785714
Q182.42857143
median92.7826087
Q3103.6470588
95-th percentile121.1741935
Maximum170
Range118
Interquartile range (IQR)21.21848739

Descriptive statistics

Standard deviation16.28865604
Coefficient of variation (CV)0.1739046787
Kurtosis1.021736141
Mean93.66427727
Median Absolute Deviation (MAD)10.6173913
Skewness0.6034859442
Sum135719.5378
Variance265.3203157
MonotocityNot monotonic
2020-12-13T14:23:53.029003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
94100.6%
 
99100.6%
 
9290.5%
 
8590.5%
 
9890.5%
 
11380.5%
 
9680.5%
 
12070.4%
 
9170.4%
 
8070.4%
 
7970.4%
 
10460.4%
 
9560.4%
 
8260.4%
 
7260.4%
 
6860.4%
 
8760.4%
 
8460.4%
 
10860.4%
 
87.560.4%
 
78.3333333360.4%
 
8160.4%
 
11150.3%
 
8850.3%
 
11050.3%
 
Other values (1011)127777.4%
 
(Missing)20012.1%
 
ValueCountFrequency (%) 
5210.1%
 
55.62510.1%
 
56.510.1%
 
57.0833333310.1%
 
57.2142857110.1%
 
57.710.1%
 
57.8888888910.1%
 
58.4285714310.1%
 
58.9166666710.1%
 
59.6666666710.1%
 
ValueCountFrequency (%) 
17010.1%
 
16110.1%
 
15910.1%
 
158.860465110.1%
 
15810.1%
 
15410.1%
 
14940.2%
 
14810.1%
 
146.520.1%
 
144.510.1%
 

min_ibp
Real number (ℝ≥0)

MISSING

Distinct69
Distinct (%)34.7%
Missing1450
Missing (%)87.9%
Infinite0
Infinite (%)0.0%
Mean81.50502513
Minimum4
Maximum155
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:53.236021image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile54
Q172
median81
Q392
95-th percentile110.1
Maximum155
Range151
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.93943162
Coefficient of variation (CV)0.2201021544
Kurtosis3.175948269
Mean81.50502513
Median Absolute Deviation (MAD)10
Skewness-0.2236649008
Sum16219.5
Variance321.8232069
MonotocityNot monotonic
2020-12-13T14:23:53.448847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7990.5%
 
6680.5%
 
8380.5%
 
7070.4%
 
7370.4%
 
7570.4%
 
9460.4%
 
9260.4%
 
8260.4%
 
8860.4%
 
9360.4%
 
8660.4%
 
7650.3%
 
8150.3%
 
7750.3%
 
8450.3%
 
7240.2%
 
8040.2%
 
8740.2%
 
6840.2%
 
8530.2%
 
7830.2%
 
6930.2%
 
9030.2%
 
10830.2%
 
Other values (44)664.0%
 
(Missing)145087.9%
 
ValueCountFrequency (%) 
410.1%
 
1610.1%
 
3310.1%
 
4010.1%
 
4210.1%
 
4810.1%
 
4920.1%
 
5310.1%
 
5420.1%
 
5810.1%
 
ValueCountFrequency (%) 
15510.1%
 
12110.1%
 
12010.1%
 
11810.1%
 
11720.1%
 
11620.1%
 
11510.1%
 
11110.1%
 
11030.2%
 
10830.2%
 

max_ibp
Real number (ℝ≥0)

MISSING

Distinct82
Distinct (%)41.2%
Missing1450
Missing (%)87.9%
Infinite0
Infinite (%)0.0%
Mean115.0150754
Minimum70
Maximum247
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:53.648414image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile81.9
Q195
median110
Q3126
95-th percentile167.2
Maximum247
Range177
Interquartile range (IQR)31

Descriptive statistics

Standard deviation28.28971284
Coefficient of variation (CV)0.2459652593
Kurtosis3.956089616
Mean115.0150754
Median Absolute Deviation (MAD)15
Skewness1.553788873
Sum22888
Variance800.3078524
MonotocityNot monotonic
2020-12-13T14:23:53.857320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
124100.6%
 
11870.4%
 
9360.4%
 
8760.4%
 
9750.3%
 
8650.3%
 
11050.3%
 
12650.3%
 
10850.3%
 
9850.3%
 
10950.3%
 
12140.2%
 
9040.2%
 
9640.2%
 
12040.2%
 
10640.2%
 
10440.2%
 
11140.2%
 
10230.2%
 
11230.2%
 
10730.2%
 
13130.2%
 
9430.2%
 
11630.2%
 
9530.2%
 
Other values (57)865.2%
 
(Missing)145087.9%
 
ValueCountFrequency (%) 
7010.1%
 
7210.1%
 
7420.1%
 
7520.1%
 
7610.1%
 
8010.1%
 
8120.1%
 
8220.1%
 
8310.1%
 
8420.1%
 
ValueCountFrequency (%) 
24710.1%
 
23210.1%
 
22410.1%
 
18920.1%
 
18610.1%
 
18010.1%
 
17420.1%
 
16910.1%
 
16710.1%
 
16410.1%
 

avg_ibp
Real number (ℝ≥0)

MISSING

Distinct178
Distinct (%)89.4%
Missing1450
Missing (%)87.9%
Infinite0
Infinite (%)0.0%
Mean96.63163467
Minimum65
Maximum181.25
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:54.104400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile70.64
Q185.75
median95.72727273
Q3106.4035088
95-th percentile124.5781818
Maximum181.25
Range116.25
Interquartile range (IQR)20.65350877

Descriptive statistics

Standard deviation16.53420957
Coefficient of variation (CV)0.1711055559
Kurtosis2.668929202
Mean96.63163467
Median Absolute Deviation (MAD)10.48027444
Skewness0.8983669526
Sum19229.6953
Variance273.3800861
MonotocityNot monotonic
2020-12-13T14:23:54.309211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9840.2%
 
9240.2%
 
9030.2%
 
10530.2%
 
98.3333333320.1%
 
6820.1%
 
101.520.1%
 
10720.1%
 
8420.1%
 
8120.1%
 
8620.1%
 
106.520.1%
 
10020.1%
 
79.6666666720.1%
 
8820.1%
 
73.610.1%
 
96.4347826110.1%
 
93.710.1%
 
83.810.1%
 
87.7241379310.1%
 
81.62510.1%
 
89.8421052610.1%
 
87.6666666710.1%
 
86.510.1%
 
92.510.1%
 
Other values (153)1539.3%
 
(Missing)145087.9%
 
ValueCountFrequency (%) 
6510.1%
 
6820.1%
 
68.610.1%
 
68.904761910.1%
 
69.0606060610.1%
 
69.9196428610.1%
 
7010.1%
 
70.3636363610.1%
 
70.410.1%
 
70.6666666710.1%
 
ValueCountFrequency (%) 
181.2510.1%
 
140.346153810.1%
 
138.916666710.1%
 
133.428571410.1%
 
129.722222210.1%
 
127.266666710.1%
 
12710.1%
 
125.977777810.1%
 
125.666666710.1%
 
125.281818210.1%
 

min_nibp_systolic
Real number (ℝ≥0)

MISSING

Distinct141
Distinct (%)9.3%
Missing129
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean120.0138158
Minimum44
Maximum236
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:54.512492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile83
Q1104
median118
Q3135
95-th percentile162
Maximum236
Range192
Interquartile range (IQR)31

Descriptive statistics

Standard deviation24.40362687
Coefficient of variation (CV)0.2033401464
Kurtosis0.6954027865
Mean120.0138158
Median Absolute Deviation (MAD)15.5
Skewness0.3831745339
Sum182421
Variance595.5370045
MonotocityNot monotonic
2020-12-13T14:23:54.708943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
110362.2%
 
116332.0%
 
115332.0%
 
133321.9%
 
112281.7%
 
131281.7%
 
122271.6%
 
119271.6%
 
113271.6%
 
90271.6%
 
108261.6%
 
128261.6%
 
136261.6%
 
111251.5%
 
124251.5%
 
120251.5%
 
121251.5%
 
106251.5%
 
104241.5%
 
105241.5%
 
114231.4%
 
107231.4%
 
125221.3%
 
103221.3%
 
127221.3%
 
Other values (116)85952.1%
 
(Missing)1297.8%
 
ValueCountFrequency (%) 
4410.1%
 
5010.1%
 
5320.1%
 
5420.1%
 
5710.1%
 
5920.1%
 
6010.1%
 
6110.1%
 
6210.1%
 
6310.1%
 
ValueCountFrequency (%) 
23610.1%
 
21410.1%
 
20810.1%
 
20620.1%
 
20410.1%
 
19410.1%
 
19220.1%
 
19110.1%
 
18910.1%
 
18830.2%
 

max_nibp_systolic
Real number (ℝ≥0)

MISSING

Distinct149
Distinct (%)9.8%
Missing129
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean165.0019737
Minimum89
Maximum250
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:54.906832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile119
Q1145
median163
Q3184
95-th percentile218
Maximum250
Range161
Interquartile range (IQR)39

Descriptive statistics

Standard deviation29.14487878
Coefficient of variation (CV)0.1766335161
Kurtosis0.006700238314
Mean165.0019737
Median Absolute Deviation (MAD)19
Skewness0.3390569577
Sum250803
Variance849.4239592
MonotocityNot monotonic
2020-12-13T14:23:55.148305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
149281.7%
 
160271.6%
 
150271.6%
 
142271.6%
 
155271.6%
 
152261.6%
 
161241.5%
 
151241.5%
 
159241.5%
 
168231.4%
 
192231.4%
 
185231.4%
 
177231.4%
 
165221.3%
 
167221.3%
 
174221.3%
 
154211.3%
 
156211.3%
 
189211.3%
 
166201.2%
 
169201.2%
 
170201.2%
 
143201.2%
 
157201.2%
 
164201.2%
 
Other values (124)94557.3%
 
(Missing)1297.8%
 
ValueCountFrequency (%) 
8910.1%
 
9120.1%
 
9310.1%
 
9420.1%
 
9810.1%
 
10110.1%
 
10310.1%
 
10410.1%
 
10620.1%
 
10720.1%
 
ValueCountFrequency (%) 
25040.2%
 
24920.1%
 
24820.1%
 
24710.1%
 
24610.1%
 
24420.1%
 
24230.2%
 
24030.2%
 
23910.1%
 
23840.2%
 

avg_nibp_systolic
Real number (ℝ≥0)

MISSING

Distinct1175
Distinct (%)77.3%
Missing129
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean141.8674282
Minimum80.85714286
Maximum236
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:55.332504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum80.85714286
5-th percentile107.15625
Q1126.5660714
median141.7142857
Q3156.4625
95-th percentile177.81
Maximum236
Range155.1428571
Interquartile range (IQR)29.89642857

Descriptive statistics

Standard deviation21.97668538
Coefficient of variation (CV)0.1549100146
Kurtosis0.09557275722
Mean141.8674282
Median Absolute Deviation (MAD)14.95095238
Skewness0.2185552942
Sum215638.4909
Variance482.9747003
MonotocityNot monotonic
2020-12-13T14:23:55.543589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13780.5%
 
15280.5%
 
14480.5%
 
12780.5%
 
11870.4%
 
15570.4%
 
14660.4%
 
14160.4%
 
13860.4%
 
11260.4%
 
12660.4%
 
12560.4%
 
16350.3%
 
13350.3%
 
12850.3%
 
14950.3%
 
14050.3%
 
17750.3%
 
11750.3%
 
11450.3%
 
13450.3%
 
16540.2%
 
11340.2%
 
140.540.2%
 
15940.2%
 
Other values (1150)137783.5%
 
(Missing)1297.8%
 
ValueCountFrequency (%) 
80.8571428610.1%
 
8610.1%
 
87.3333333310.1%
 
89.4285714310.1%
 
9120.1%
 
9220.1%
 
92.310.1%
 
92.7727272710.1%
 
9310.1%
 
93.8292682910.1%
 
ValueCountFrequency (%) 
23610.1%
 
230.666666710.1%
 
215.610.1%
 
20810.1%
 
207.705882410.1%
 
20620.1%
 
20410.1%
 
203.461538510.1%
 
200.142857110.1%
 
196.7510.1%
 

min_ibp_systolic
Real number (ℝ≥0)

MISSING

Distinct81
Distinct (%)55.9%
Missing1504
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean123.4
Minimum7
Maximum191
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:55.739048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile86.4
Q1106
median124
Q3140
95-th percentile170
Maximum191
Range184
Interquartile range (IQR)34

Descriptive statistics

Standard deviation28.31774081
Coefficient of variation (CV)0.229479261
Kurtosis1.396107373
Mean123.4
Median Absolute Deviation (MAD)18
Skewness-0.269879536
Sum17893
Variance801.8944444
MonotocityNot monotonic
2020-12-13T14:23:55.958749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12870.4%
 
13450.3%
 
10750.3%
 
10640.2%
 
14040.2%
 
12540.2%
 
9730.2%
 
16230.2%
 
13030.2%
 
12730.2%
 
9830.2%
 
12030.2%
 
8830.2%
 
15830.2%
 
10930.2%
 
12130.2%
 
10230.2%
 
10030.2%
 
9320.1%
 
13520.1%
 
12220.1%
 
17020.1%
 
11620.1%
 
11020.1%
 
11220.1%
 
Other values (56)664.0%
 
(Missing)150491.2%
 
ValueCountFrequency (%) 
710.1%
 
4710.1%
 
6010.1%
 
7210.1%
 
7510.1%
 
8010.1%
 
8210.1%
 
8610.1%
 
8830.2%
 
8910.1%
 
ValueCountFrequency (%) 
19110.1%
 
18710.1%
 
18410.1%
 
18110.1%
 
17610.1%
 
17510.1%
 
17410.1%
 
17020.1%
 
16620.1%
 
16510.1%
 

max_ibp_systolic
Real number (ℝ≥0)

MISSING

Distinct82
Distinct (%)56.6%
Missing1504
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean162.0689655
Minimum103
Maximum288
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:56.171232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile116.4
Q1140
median162
Q3180
95-th percentile227.2
Maximum288
Range185
Interquartile range (IQR)40

Descriptive statistics

Standard deviation32.58400578
Coefficient of variation (CV)0.2010502485
Kurtosis1.45971005
Mean162.0689655
Median Absolute Deviation (MAD)21
Skewness0.8978096231
Sum23500
Variance1061.717433
MonotocityNot monotonic
2020-12-13T14:23:56.404438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16860.4%
 
18050.3%
 
15540.2%
 
17040.2%
 
13440.2%
 
19440.2%
 
14630.2%
 
16530.2%
 
18330.2%
 
11930.2%
 
14130.2%
 
15030.2%
 
16230.2%
 
14030.2%
 
17430.2%
 
15130.2%
 
18830.2%
 
14320.1%
 
19920.1%
 
17220.1%
 
14420.1%
 
11620.1%
 
13920.1%
 
13120.1%
 
13720.1%
 
Other values (57)694.2%
 
(Missing)150491.2%
 
ValueCountFrequency (%) 
10310.1%
 
10810.1%
 
10920.1%
 
11210.1%
 
11410.1%
 
11620.1%
 
11820.1%
 
11930.2%
 
12010.1%
 
12210.1%
 
ValueCountFrequency (%) 
28810.1%
 
25710.1%
 
24710.1%
 
24510.1%
 
24310.1%
 
24210.1%
 
23310.1%
 
23010.1%
 
21610.1%
 
20910.1%
 

avg_ibp_systolic
Real number (ℝ≥0)

MISSING

Distinct135
Distinct (%)93.1%
Missing1504
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean142.4529709
Minimum86.45454545
Maximum209.4615385
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:56.612461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum86.45454545
5-th percentile106.3711111
Q1124
median140
Q3157.3529412
95-th percentile190.0761905
Maximum209.4615385
Range123.006993
Interquartile range (IQR)33.35294118

Descriptive statistics

Standard deviation24.7602468
Coefficient of variation (CV)0.1738134814
Kurtosis-0.05095780292
Mean142.4529709
Median Absolute Deviation (MAD)16.75
Skewness0.3607498767
Sum20655.68078
Variance613.0698218
MonotocityNot monotonic
2020-12-13T14:23:56.829203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13840.2%
 
12420.1%
 
13620.1%
 
141.333333320.1%
 
17020.1%
 
15420.1%
 
12620.1%
 
12320.1%
 
129.910.1%
 
152.110.1%
 
191.2510.1%
 
117.666666710.1%
 
136.833333310.1%
 
15710.1%
 
122.510.1%
 
161.510.1%
 
161.428571410.1%
 
112.510.1%
 
178.510.1%
 
15310.1%
 
86.4545454510.1%
 
111.2510.1%
 
156.510.1%
 
119.7510.1%
 
157.666666710.1%
 
Other values (110)1106.7%
 
(Missing)150491.2%
 
ValueCountFrequency (%) 
86.4545454510.1%
 
87.9333333310.1%
 
98.6190476210.1%
 
100.666666710.1%
 
101.810.1%
 
101.894736810.1%
 
10410.1%
 
105.888888910.1%
 
108.310.1%
 
11010.1%
 
ValueCountFrequency (%) 
209.461538510.1%
 
20510.1%
 
203.510.1%
 
197.2510.1%
 
19410.1%
 
192.810.1%
 
191.2510.1%
 
190.428571410.1%
 
188.666666710.1%
 
187.428571410.1%
 

glucose
Real number (ℝ≥0)

Distinct271
Distinct (%)16.5%
Missing11
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean144.0042735
Minimum38
Maximum671
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:57.025933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile86
Q1101
median122
Q3154
95-th percentile292.15
Maximum671
Range633
Interquartile range (IQR)53

Descriptive statistics

Standard deviation72.04124324
Coefficient of variation (CV)0.5002715647
Kurtosis10.14991861
Mean144.0042735
Median Absolute Deviation (MAD)24
Skewness2.744189624
Sum235879
Variance5189.940727
MonotocityNot monotonic
2020-12-13T14:23:57.221759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
96352.1%
 
102271.6%
 
97261.6%
 
101261.6%
 
104251.5%
 
90251.5%
 
120251.5%
 
118241.5%
 
110231.4%
 
111231.4%
 
115231.4%
 
99231.4%
 
98231.4%
 
93231.4%
 
128221.3%
 
108221.3%
 
105221.3%
 
132211.3%
 
103211.3%
 
127211.3%
 
95211.3%
 
135201.2%
 
100201.2%
 
86201.2%
 
94191.2%
 
Other values (246)105864.2%
 
ValueCountFrequency (%) 
3810.1%
 
4210.1%
 
5210.1%
 
5610.1%
 
6410.1%
 
6610.1%
 
6810.1%
 
6910.1%
 
7130.2%
 
7310.1%
 
ValueCountFrequency (%) 
67110.1%
 
66310.1%
 
59610.1%
 
56520.1%
 
56210.1%
 
51210.1%
 
50210.1%
 
49410.1%
 
49110.1%
 
48910.1%
 

creatinine
Real number (ℝ≥0)

MISSING

Distinct254
Distinct (%)15.6%
Missing22
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean1.21213276
Minimum0.27
Maximum18.8
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:57.425473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.27
5-th percentile0.59
Q10.8
median0.98
Q31.24
95-th percentile2.551
Maximum18.8
Range18.53
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation1.124710787
Coefficient of variation (CV)0.9278775594
Kurtosis81.70938604
Mean1.21213276
Median Absolute Deviation (MAD)0.22
Skewness7.570573578
Sum1972.14
Variance1.264974354
MonotocityNot monotonic
2020-12-13T14:23:57.644467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.9784.7%
 
0.8724.4%
 
1643.9%
 
0.7623.8%
 
1.1593.6%
 
0.6432.6%
 
1.2291.8%
 
0.82231.4%
 
0.88231.4%
 
0.83221.3%
 
0.85211.3%
 
0.74191.2%
 
0.96191.2%
 
0.99181.1%
 
0.98181.1%
 
1.4181.1%
 
0.71171.0%
 
0.81171.0%
 
0.84171.0%
 
0.91171.0%
 
1.15171.0%
 
0.87161.0%
 
0.89161.0%
 
0.77150.9%
 
1.03150.9%
 
Other values (229)89254.1%
 
(Missing)221.3%
 
ValueCountFrequency (%) 
0.2710.1%
 
0.3210.1%
 
0.3510.1%
 
0.3710.1%
 
0.3810.1%
 
0.420.1%
 
0.4110.1%
 
0.4210.1%
 
0.4310.1%
 
0.4410.1%
 
ValueCountFrequency (%) 
18.810.1%
 
1610.1%
 
12.8210.1%
 
12.110.1%
 
11.610.1%
 
10.910.1%
 
9.610.1%
 
8.9410.1%
 
8.6110.1%
 
8.310.1%
 

gcs
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)0.9%
Missing232
Missing (%)14.1%
Infinite0
Infinite (%)0.0%
Mean12.67607622
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Memory size13.0 KiB
2020-12-13T14:23:57.808725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q111
median14
Q315
95-th percentile15
Maximum15
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.291437164
Coefficient of variation (CV)0.2596574135
Kurtosis0.899036991
Mean12.67607622
Median Absolute Deviation (MAD)1
Skewness-1.405492886
Sum17962
Variance10.8335586
MonotocityNot monotonic
2020-12-13T14:23:57.986348image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
1569442.1%
 
1418711.3%
 
12804.9%
 
13754.5%
 
11724.4%
 
10613.7%
 
6543.3%
 
7523.2%
 
9442.7%
 
3372.2%
 
8362.2%
 
5140.8%
 
4110.7%
 
(Missing)23214.1%
 
ValueCountFrequency (%) 
3372.2%
 
4110.7%
 
5140.8%
 
6543.3%
 
7523.2%
 
8362.2%
 
9442.7%
 
10613.7%
 
11724.4%
 
12804.9%
 
ValueCountFrequency (%) 
1569442.1%
 
1418711.3%
 
13754.5%
 
12804.9%
 
11724.4%
 
10613.7%
 
9442.7%
 
8362.2%
 
7523.2%
 
6543.3%
 
Distinct2
Distinct (%)0.2%
Missing447
Missing (%)27.1%
Memory size13.0 KiB
0
877 
1
325 
(Missing)
447 
ValueCountFrequency (%) 
087753.2%
 
132519.7%
 
(Missing)44727.1%
 
2020-12-13T14:23:58.120913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing447
Missing (%)27.1%
Memory size13.0 KiB
0
1119 
1
 
83
(Missing)
447 
ValueCountFrequency (%) 
0111967.9%
 
1835.0%
 
(Missing)44727.1%
 
2020-12-13T14:23:58.202100image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

heparin_iv
Boolean

MISSING

Distinct2
Distinct (%)0.2%
Missing447
Missing (%)27.1%
Memory size13.0 KiB
0
1002 
1
200 
(Missing)
447 
ValueCountFrequency (%) 
0100260.8%
 
120012.1%
 
(Missing)44727.1%
 
2020-12-13T14:23:58.284925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

inotropes
Boolean

MISSING

Distinct2
Distinct (%)0.2%
Missing447
Missing (%)27.1%
Memory size13.0 KiB
0
1125 
1
 
77
(Missing)
447 
ValueCountFrequency (%) 
0112568.2%
 
1774.7%
 
(Missing)44727.1%
 
2020-12-13T14:23:58.367543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

antiplatelets
Boolean

MISSING

Distinct2
Distinct (%)0.2%
Missing447
Missing (%)27.1%
Memory size13.0 KiB
0
992 
1
210 
(Missing)
447 
ValueCountFrequency (%) 
099260.2%
 
121012.7%
 
(Missing)44727.1%
 
2020-12-13T14:23:58.448761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

anticoag
Boolean

MISSING

Distinct2
Distinct (%)0.2%
Missing447
Missing (%)27.1%
Memory size13.0 KiB
0
1193 
1
 
9
(Missing)
447 
ValueCountFrequency (%) 
0119372.3%
 
190.5%
 
(Missing)44727.1%
 
2020-12-13T14:23:58.560135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing447
Missing (%)27.1%
Memory size13.0 KiB
0
1168 
1
 
34
(Missing)
447 
ValueCountFrequency (%) 
0116870.8%
 
1342.1%
 
(Missing)44727.1%
 
2020-12-13T14:23:58.642518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hypertension
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing44
Missing (%)2.7%
Memory size13.0 KiB
1
1005 
0
600 
(Missing)
 
44
ValueCountFrequency (%) 
1100560.9%
 
060036.4%
 
(Missing)442.7%
 
2020-12-13T14:23:58.743483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing44
Missing (%)2.7%
Memory size13.0 KiB
0
1339 
1
266 
(Missing)
 
44
ValueCountFrequency (%) 
0133981.2%
 
126616.1%
 
(Missing)442.7%
 
2020-12-13T14:23:58.817582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing44
Missing (%)2.7%
Memory size13.0 KiB
0
1551 
1
 
54
(Missing)
 
44
ValueCountFrequency (%) 
0155194.1%
 
1543.3%
 
(Missing)442.7%
 
2020-12-13T14:23:58.917259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

atrial_fibrillation
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing44
Missing (%)2.7%
Memory size13.0 KiB
0
1325 
1
280 
(Missing)
 
44
ValueCountFrequency (%) 
0132580.4%
 
128017.0%
 
(Missing)442.7%
 
2020-12-13T14:23:59.074546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing44
Missing (%)2.7%
Memory size13.0 KiB
0
1477 
1
 
128
(Missing)
 
44
ValueCountFrequency (%) 
0147789.6%
 
11287.8%
 
(Missing)442.7%
 
2020-12-13T14:23:59.152913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

previous_stroke
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing44
Missing (%)2.7%
Memory size13.0 KiB
0
1264 
1
341 
(Missing)
 
44
ValueCountFrequency (%) 
0126476.7%
 
134120.7%
 
(Missing)442.7%
 
2020-12-13T14:23:59.233758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

diabetes
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing44
Missing (%)2.7%
Memory size13.0 KiB
0
1304 
1
301 
(Missing)
 
44
ValueCountFrequency (%) 
0130479.1%
 
130118.3%
 
(Missing)442.7%
 
2020-12-13T14:23:59.315709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hosp_mortality
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing23
Missing (%)1.4%
Memory size13.0 KiB
0
1459 
1
167 
(Missing)
 
23
ValueCountFrequency (%) 
0145988.5%
 
116710.1%
 
(Missing)231.4%
 
2020-12-13T14:23:59.392185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2020-12-13T14:21:51.779655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:52.122908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:52.355533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:52.547205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:52.719625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:52.972672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:53.159916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:53.318680image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:53.477970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:53.688412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:54.315539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:54.759385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:54.935547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:55.153354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:55.362894image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:55.574968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:55.789928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:55.968089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:56.282048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:56.583392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:56.876267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:57.058497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:57.207060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:57.366848image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:57.524987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:57.710964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:57.904123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:58.063557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:58.217544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:58.402395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:58.564169image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:58.759898image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:58.917857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:59.087573image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:59.242217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:59.412111image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:21:59.752947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:00.012442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:00.344144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:00.555321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:00.723115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:00.894267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:01.071025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:01.244118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:01.404326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:01.557533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:01.831613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:02.047145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:02.209382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:02.500432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:02.768545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:02.949434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:03.139333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:03.340574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:03.557418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:03.793806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:03.980069image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:04.147950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:04.327311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:04.500148image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:04.681572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:04.882159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:05.033304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:05.182383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:05.335035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:05.487732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:05.650729image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:05.794826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:05.983605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:06.169377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:06.391667image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:06.605561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:06.803945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:07.018986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:07.213781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:07.378677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:07.549226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:07.709938image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:07.858108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:08.066353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:08.225981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:08.522964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:08.689322image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:08.865982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:09.039221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:09.194891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:09.402165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:09.579842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:09.751237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:09.986101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:10.167583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:10.338826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:10.520572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:10.700967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:10.882275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:11.096626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:11.279123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:11.509506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:11.706171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:11.918058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:12.097957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:12.360710image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:12.553114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:12.759935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:13.015487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:13.218494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:13.452832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:13.634083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:13.823618image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:14.041126image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:14.221710image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:14.384811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:14.583893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:14.849576image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:15.412632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:16.136807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:16.503763image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:16.772910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:17.146087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:17.778923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:18.018494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:18.444910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:18.618392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:18.781292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:18.973504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:19.121075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:19.282297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:19.424644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:19.564745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:19.765279image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:19.917259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:20.069199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:20.213661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:20.392713image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:20.705191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:21.127432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:21.431038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:22:21.695482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-13T14:23:18.660847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:23:24.238921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-13T14:23:59.706963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-13T14:24:00.211296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-13T14:24:00.679899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-13T14:24:01.149402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-13T14:24:01.538090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-13T14:23:24.852984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:23:26.773250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:23:27.887306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:23:28.934662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

patientunitstayidgenderageethnicityapacheadmissiondxadmissionheighthospitaldischargelocationhospitaldischargestatusunittypeadmissionweightdischargeweighticu_los_hoursis_primarymin_nibpmax_nibpavg_nibpmin_ibpmax_ibpavg_ibpmin_nibp_systolicmax_nibp_systolicavg_nibp_systolicmin_ibp_systolicmax_ibp_systolicavg_ibp_systolicglucosecreatininegcsantihypertensive_iv_tight_controlantihypertensive_iv_non_tight_controlheparin_ivinotropesantiplateletsanticoagantihypertensive_non_ivhypertensionischemic_heart_diseaseperipheral_vascular_diseaseatrial_fibrillationtransient_ischemic_attackprevious_strokediabeteshosp_mortality
0142974Female54African AmericanCVA, cerebrovascular accident/stroke157.5HomeAliveMed-Surg ICU117.9112.2651NaNNaNNaN59.0116.089.60000084.0153.0115.769231NaNNaNNaN127.01.1013.01.00.00.00.00.00.00.00.00.00.00.00.00.01.00.0
1143596Male83CaucasianCVA, cerebrovascular accident/stroke170.2RehabilitationAliveNeuro ICU91.695.8431NaNNaNNaN96.0164.0122.600000158.0198.0177.400000NaNNaNNaN111.00.8615.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.0
2147575Male66CaucasianCVA, cerebrovascular accident/stroke177.8RehabilitationAliveCCU-CTICU70.170.3631NaNNaNNaN60.091.076.40000084.0144.0107.727273NaNNaNNaN114.01.0315.00.00.00.00.00.00.00.01.01.00.00.00.00.00.00.0
3153552Female63CaucasianCVA, cerebrovascular accident/stroke160.0HomeAliveMed-Surg ICUNaN72.6171NaNNaNNaN90.0111.0101.250000130.0168.0146.600000NaNNaNNaN100.01.0115.0NaNNaNNaNNaNNaNNaNNaN1.00.00.00.00.01.00.00.0
4166480Female70HispanicCVA, cerebrovascular accident/stroke154.9Skilled Nursing FacilityAliveMed-Surg ICU112.5104.9471NaNNaNNaN59.0112.079.15789595.0192.0129.321429NaNNaNNaN138.01.987.00.00.01.00.00.00.00.01.00.00.00.00.01.00.00.0
5174480Female79CaucasianCVA, cerebrovascular accident/stroke167.6RehabilitationAliveNeuro ICU83.975.7471NaNNaNNaN81.0125.0100.366667112.0204.0157.419355138.0170.0153.818182149.00.8814.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
6180550Male68CaucasianCVA, cerebrovascular accident/stroke181.6HomeAliveCTICU92.686.3171NaNNaNNaN87.087.087.00000090.094.092.000000NaNNaNNaN105.01.4215.00.00.00.00.00.00.00.01.01.00.01.00.00.00.00.0
7182888Male65CaucasianCVA, cerebrovascular accident/stroke175.3HomeAliveMed-Surg ICUNaN101.8511NaNNaNNaN70.0110.087.724138104.0148.0119.033333NaNNaNNaN205.01.06NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
8187644Male73CaucasianCVA, cerebrovascular accident/stroke175.3HomeAliveCCU-CTICUNaN54.2361NaNNaNNaN73.0122.0101.692308116.0227.0184.047619NaNNaNNaN129.00.8814.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.0
9196657Male57CaucasianCVA, cerebrovascular accident/stroke167.6DeathExpiredMed-Surg ICU83.385.21381NaNNaNNaN75.0189.0111.302326102.0227.0153.279070NaNNaNNaN138.01.0911.00.00.00.01.00.00.00.01.00.00.00.00.00.00.01.0

Last rows

patientunitstayidgenderageethnicityapacheadmissiondxadmissionheighthospitaldischargelocationhospitaldischargestatusunittypeadmissionweightdischargeweighticu_los_hoursis_primarymin_nibpmax_nibpavg_nibpmin_ibpmax_ibpavg_ibpmin_nibp_systolicmax_nibp_systolicavg_nibp_systolicmin_ibp_systolicmax_ibp_systolicavg_ibp_systolicglucosecreatininegcsantihypertensive_iv_tight_controlantihypertensive_iv_non_tight_controlheparin_ivinotropesantiplateletsanticoagantihypertensive_non_ivhypertensionischemic_heart_diseaseperipheral_vascular_diseaseatrial_fibrillationtransient_ischemic_attackprevious_strokediabeteshosp_mortality
1639426000Female26HispanicCVA, cerebrovascular accident/stroke160.0DeathExpiredMed-Surg ICU50.8NaN102174.092.085.857143NaNNaNNaN124.0138.0132.142857NaNNaNNaN209.00.3715.00.00.00.00.00.00.00.0NaNNaNNaNNaNNaNNaNNaN1.0
16403060654Male77CaucasianCVA, cerebrovascular accident/stroke180.3HomeAliveMed-Surg ICU99.8NaN69182.0117.0102.166667NaNNaNNaN104.0135.0122.166667NaNNaNNaN102.01.2615.00.00.00.00.00.00.00.0NaNNaNNaNNaNNaNNaNNaN0.0
1641432018Male54HispanicCVA, cerebrovascular accident/stroke188.0DeathExpiredNeuro ICU145.0NaN73163.0105.080.222222NaNNaNNaN86.0168.0121.666667NaNNaNNaN491.01.403.00.00.00.01.00.00.00.0NaNNaNNaNNaNNaNNaNNaN1.0
16421018904Female79CaucasianCVA, cerebrovascular accident/stroke157.0HomeAliveNeuro ICU50.748.3160184.0107.092.71428686.0113.095.266667108.0146.0126.714286120.0162.0134.333333175.00.707.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0
16433104132Male62Other/UnknownCVA, cerebrovascular accident/stroke170.2RehabilitationAliveSICU68.968.9671103.0132.0117.090909NaNNaNNaN149.0201.0172.272727NaNNaNNaN90.00.9414.01.00.00.00.01.00.00.0NaNNaNNaNNaNNaNNaNNaN0.0
16443104092Female74CaucasianCVA, cerebrovascular accident/stroke160.0HomeAliveSICU58.9NaN34087.093.091.250000NaNNaNNaN144.0150.0146.750000NaNNaNNaN128.00.7314.01.00.01.00.00.00.00.0NaNNaNNaNNaNNaNNaNNaN0.0
16451603641Female69CaucasianCVA, cerebrovascular accident/stroke160.0HomeAliveMed-Surg ICU74.882.114087.0116.0101.500000NaNNaNNaN136.0189.0166.250000NaNNaNNaN212.01.7015.01.01.00.00.01.00.00.0NaNNaNNaNNaNNaNNaNNaN0.0
16461048682Female36CaucasianCVA, cerebrovascular accident/stroke157.0OtherAliveNeuro ICU54.7NaN34057.078.064.800000NaNNaNNaN90.0107.095.900000NaNNaNNaN102.00.508.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0
16473124122Female85CaucasianCVA, cerebrovascular accident/stroke154.9Skilled Nursing FacilityAliveMed-Surg ICU48.448.025168.085.079.333333NaNNaNNaN105.0137.0124.500000NaNNaNNaN135.00.6815.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0
16483038473Female64CaucasianCVA, cerebrovascular accident/stroke163.0RehabilitationAliveSICUNaNNaN00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN92.00.71NaN0.00.00.00.01.00.00.0NaNNaNNaNNaNNaNNaNNaN0.0